When I started working on Wildfire Command (known as Sim Firefighter at the time), I had a pretty solid idea of what I wanted to focus on – procedural map generation. I love the challenge of making systems that feel organic and unpredictable, which is exactly what we needed for our wildfire simulation. What I didn’t anticipate was how deep I’d end up diving into algorithms and how much I’d come to appreciate Perlin noise, BSP partitioning, and cellular automata.
Favorite Technology: Perlin Noise
If I had to pick one piece of tech that really clicked for me during this project, it’s Perlin noise. Before this, I had used procedural generation a bit in other project, but never in a way where it directly impacted the project so significantly. The terrain in Wildfire Command isn’t just a static backdrop – it affects fire spread, suppression tactics, and overall strategy. Using Perlin noise allowed us to generate natural-looking landscapes with forests, dry grass patches, and water that actually make sense visually and mechanically.
The coolest part? Perlin noise creates smooth gradients instead of harsh transitions, so terrain flows naturally instead of looking like a random mess of tiles.
Cellular Automata: A Great Fit for the Project
Another key tool in our map generation system is cellular automata. Unlike Perlin noise, which generates terrain smoothly, cellular automata are all about rule-based evolution – defining behaviors that change over time based on neighboring tiles. This made it a great fit for refining terrain clusters, ensuring that forests formed in realistic patches and transitions between terrain types looked natural.
I have enjoyed working with cellular automata because it has given us a lot of control while still allowing for emergent patterns. By tweaking a few rules, we could dramatically change how the map evolved, which has been satisfying to experiment with.
What I’d Change
If I had unlimited time, I’d love to experiment with multi-layered Perlin noise to add even more depth to the terrain. Right now, we use a single layer to define basic terrain features, but layering different noise functions could allow for more nuanced environmental details – like differentiating between dense and sparse forest regions or adding subtle elevation changes that impact fire behavior.
Looking Ahead
Overall, the combination of Perlin noise, BSP partitioning, and cellular automata has been a solid foundation for our game’s map generation. It’s been awesome seeing everything come together – especially when the procedural maps start looking like real landscapes with clear wildfire risk zones.
Next up, I’m excited to see how our fire spread mechanics interact with the terrain. Wildfires are unpredictable, and that’s something we really want to capture in our simulation. So far, it’s been a fun challenge, and I can’t wait to share more about how we’re balancing realism with engaging gameplay.
Cat Time
As always, here’s a picture of one of my cats! This time, it’s Maggie, who has been supervising my work from the comfort of the couch near my desk. She’s clearly unimpressed with my debugging process, but she enjoys watching the maps generate.
